- Title
- Predicting Inhibitors of Acetylcholinesterase by Regression and Classification Machine Learning Approaches with Combinations of Molecular Descriptors
- Creators
- Dmitriy CHEKMAREV - Department of Pharmacology and Environmental Bioinformatics & Computational Toxicology Center (ebCTC), University of Medicine & Dentistry of New Jersey, Robert Wood Johnson Medical School, 675 Hoes Lane, Piscataway, New Jersey 08854, United StatesVladyslav KHOLODOVYCH - Department of Pharmacology and Environmental Bioinformatics & Computational Toxicology Center (ebCTC), University of Medicine & Dentistry of New Jersey, Robert Wood Johnson Medical School, 675 Hoes Lane, Piscataway, New Jersey 08854, United StatesSandhya KORTAGERE - Department of Pharmacology and Environmental Bioinformatics & Computational Toxicology Center (ebCTC), University of Medicine & Dentistry of New Jersey, Robert Wood Johnson Medical School, 675 Hoes Lane, Piscataway, New Jersey 08854, United StatesWilliam J WELSH - Department of Pharmacology and Environmental Bioinformatics & Computational Toxicology Center (ebCTC), University of Medicine & Dentistry of New Jersey, Robert Wood Johnson Medical School, 675 Hoes Lane, Piscataway, New Jersey 08854, United StatesSean EKINS - Department of Pharmacology and Environmental Bioinformatics & Computational Toxicology Center (ebCTC), University of Medicine & Dentistry of New Jersey, Robert Wood Johnson Medical School, 675 Hoes Lane, Piscataway, New Jersey 08854, United States
- Publication Details
- Pharmaceutical research, v 26(9), pp 2216-2224
- Publisher
- Springer; New York, NY
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- Microbiology and Immunology
- Web of Science ID
- WOS:000268584700018
- Scopus ID
- 2-s2.0-68149125488
- Other Identifier
- 991014877885004721
Journal article
Predicting Inhibitors of Acetylcholinesterase by Regression and Classification Machine Learning Approaches with Combinations of Molecular Descriptors
Pharmaceutical research, v 26(9), pp 2216-2224
2009
PMID: 19603258
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- Collaboration types
- Domestic collaboration
- Web of Science research areas
- Chemistry, Multidisciplinary
- Pharmacology & Pharmacy